What makes Happiness?

For a long time, people have been interested in what makes us happy and how we can improve well-being in society. One big question is whether having more money or wealth actually makes people happier, or if there are other things that matter more. We often hear the saying “money can’t buy happiness,” but the real answer is a bit more complicated than that.

In this data story, we will explore how happiness relates to different economic and social factors using information from the World Happiness Report 2019 and World Development Indicators. This report looks at how happy people are in different countries and compares that with demographic variables like GDP per capita, Gross National Income (GNI), the gini index, unemployment rates, education levels, and life expectancy.

We want to find out if richer countries really have happier people, and if so, how strong this connection is. But we will also look beyond money to see how things like having a job, going to school, and living a long, healthy life affect happiness. For example, being unemployed might make people less happy even if their country is wealthy, and having a good education could improve well-being in ways that money alone can’t.

First, we will compare happiness scores with income indicators like GDP per capita and GNI. Then, we will analyze how unemployment rates relate to happiness. After that, we will look at the gini index, education and life expectancy to see how these factors could affect happiness scores across countries.

By comparing these different aspects, we hope to better understand what really contributes to happiness around the world. This will help us see whether the saying “money buys happiness” really holds. And if it doesn’t hold, we could find out what does contribute to happiness.

Money does buy happiness

It is often proposed that greater national wealth leads to higher levels of happiness among citizens. To see wether they are correlated, we analysed how average happiness scores relate to three key economic indicators: Gross Domestic Product (GDP), Gross National Income (GNI), and the Gini Index (Income inequality).

The trendlines suggest that countries with higher GDP and GNI often report higher happiness levels. As one moves along the GDP and GNI axis more countries appear above the trendline and less appear below it. This supports the general belief that wealth leads to happiness due to the fact that wealth increases access to basic needs, and social stability, factors that are positively associated with well-being and happiness. However, the relationship is not linear, and several wealthy countries show only moderate happiness scores, this suggests other factors are still extremely relevant and happiness cannot be predicted by looking at wealth alone. Correlation does not imply causation, and the observed pattern may be influenced by unmeasured factors.

The third plot, Happiness vs Gini Index, presents a different insight. Within this dataset, a negative correlation is visible, with happiness tending to decline as income inequality (measured by the Gini Index) increases. . In other words, even among wealthier nations, greater inequality is associated with lower average happiness. This aligns with findings by researchers such as Oishi et al., (2011), who in their research found that americans reported being happier on average in years when the national income inequality was less. This suggests income inequality might be a factor which has impact on the happiness . However income inequality may be acting as a substitute for how people perceive the fairness in society and the level of social mobility people have. Happiness is also a subjective measurement, and cultural differences in how inequality is perceived can vary widely, potentially affecting how people respond in surveys.

The plots suggest that while wealth does matter, its distribution may be even more of a factor. Factors such as income equality are likely important to how people will respond to questions about their happiness.

The figure below shows a graph with the ranking of Gini index, GDP and GNI compared to the happiness. This way a mismatch can be seen and the amount of extreme cases is visible. When the mismatch is >30, the line turns red. For the gini index this is slightly difference, the line is red when the mismatch is >20. This is because there is a lot of missing data for gini index, so the sample rate is smaller. Therefore a mismatch of >30 has a way bigger impact than it has for GDP or GNI. The plot about the gini index and happiness shows a large amount of mismatches between the two variables suggesting the correlation between the two might not be as strong as it seemed from previous representations. This is in stark contrast to the plots about GDP per capita and GNI per capita, these variables seem te accurately predict and match their respective ranking within the happiness index. This suggests a positive correlation between the GDP per capita and GNI per capita, and thus the wealth, and the happiness reported in a country.

Money does not buy happiness

Economic wealth could explain some of the variation in national happiness. However, other societal indicators may offer different insights. To further investigate the what influences the way people perceive their happiness, we examined its relationship with education expenditure, unemployment rate, and life expectancy.

In the plot about Happiness vs Education Expenditure, there is a slight positive trend. Countries that allocate a higher percentage of their GDP to education generally report higher happiness levels according to the dataset. It suggests that investment in quality education contributes to societal well-being and happiness.

The plot about Happiness vs Unemployment Rate has a slight negative trendline: as unemployment increases, average happiness declines according to the dataset. This is a result in line with previous research like Winkelmann (2014), which found that unemployment consistently and significantly reduces life satisfaction and caused a reduction in the happiness score recorded, which might be due to the economic insecurity and the psychological toll of uncertainty.

In the plot about Happiness vs Life Expectancy, a positive correlation can be read from the regression line. Countries where people live longer also tend to report higher happiness scores. This relationship is likely tied to broader indicators of public health and quality of life. As with education, longer life expectancy reflects better healthcare systems, better nutrition, and a better environments, all of this tends to go hand-in-hand with happiness.

The figure below shows a graph with the ranking of Life expectancy, Unemployment rate and Educational expenditure compared to the happiness. The plot showing life expectancy versus happiness contains a large amount of seemingly random mismatches suggesting the correlation between the two variables not quite strong. The plot showing unemployment rate versus happiness shows a comparatively large amount of mismatches where high ranking countries in one variable seem to be low comparatively low ranked in the other. This suggests a negative correlation between unemployment rate and happiness. The connection between happiness and education expenditure seems low due to the fact that the countries that have high/low spending on education can either have a very low happiness ranking or a very high one unrelated to their education spending. The top country in education spending, Namibia has a happiness ranking of 89 for example. Compared to the economic mismatch graph, this one shows a similar pattern: red lines are abundant, suggesting that single metrics (whether income or life expectancy) fail to fully explain happiness. Both graphs point to the same conclusion—happiness is multidimensional. ik heet mees